ASDEX Upgrade Team’s research while affiliated with Max Planck Institute for Plasma Physics and other places

A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modelling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g., to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modelling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of Integrated Data Analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

Theoretical studies have suggested that nonlinear effects can lead to "radio frequency condensation", which coalesces RF power deposition and driven current near the center of a magnetic island. It is predicted that an initially broad current profile can coalesce in islands when they reach sufficient width, providing automatic stabilization. Experimental validation of the theory has thus far been lacking. This paper proposes experiments on DIII-D for testing and refining the theory of the nonlinear effects.

Methodologies for combining the accuracy of data-driven models with extrapolability of physics-based models are described and tested, for the task of building transport models of tokamak fusion reactors that extrapolate well to new operational regimes. Information from multiple physics simulations (the ASTRA transport code with gyro-Bohm and TGLF estimates for turbulent diffusion) as well as multiple distinct experiments (DIII-D and AUG tokamaks) are considered. Applications of the methodology to the task of commissioning and controlling a new reactor such as ITER are discussed.

In this paper we demonstrate how the inversion, in energy and major radius (E, R) coordinates, of imaging neutral particle analyser (INPA) measurements can be used to obtain the fast-ion distribution. The INPA is most sensitive to passing ions with energies in the range (20-150) keV and pitches near 0.5 in the core and 0.7 near the plasma edge. Inversion of synthetic signals, via 0th -order Tikhonov and Elastic Net regularization, were performed to demonstrate the capability of recovering the ground truth fast-ion 2D phase-space distri- bution resolved in major radius and energy, even in the presence of moderate noise levels (10%). Finally, we apply our method to measure the 2D phase-space distribution in an MHD quiescent plasma at ASDEX Upgrade and find good agreement with the neoclassical fast-ion distribution predicted by TRANSP.

An Imaging Neutral Particle Analyser (INPA) diagnostic has been installed and commissioned at the ASDEX Upgrade (AUG) tokamak. The AUG INPA diagnostic provides energy and radially resolved measurements of the fast-ion (FI) distribution, complementing the existing set of diagnostic which measure the confined FI population. To this end, it analyses charge exchange (CX) neutrals produced in reactions between FI and neutrals injected by a neutral beam injector. These CX neutrals are ionized by a 20 nm carbon foil and deflected towards a scintillator by the machine magnetic field. The use of a scintillator as active component allows us to cover the whole plasma radial range with an energy resolution of 9 keV and a spatial of 7 cm for 93 keV deuterons. First measurements demonstrate the high sensitivity of the INPA diagnostic to different AUG fast-ion distribution functions, from NBI and ion-cyclotron resonance heating (ICRH) origin, and show good agreement with the synthetic diagnostic.

In this work, we demonstrate the utility of state representation learning applied to modeling the time evolution of electron density and temperature profiles at ASDEX-Upgrade (AUG). The proposed model is a deep neural network which learns to map the high dimensional profile observations to a lower dimensional state. The mapped states, alongside the original profile's corresponding machine parameters are used to learn a forward model to propagate the state in time. We show that this approach is able to predict AUG discharges using only a selected set of machine parameters. The state is then further conditioned to encode information about the confinement regime, which yields a simple baseline linear classifier, while still retaining the information needed to predict the evolution of profiles. We then discuss the potential use cases and limitations of state representation learning algorithms applied to fusion devices.

Core particle fueling, an essential task in the European demonstration fusion power plant EU-DEMO, relies on adequate pellet injection. However, pellets are fragile objects, and their delivery efficiency can hardly be assumed to be unity. Exploring kinetic control of the EU-DEMO1 scenario indicates that such missed-out pellets do cause a considerable problem for keeping a burning plasma. Missed-out pellets can cause a severe drop of plasma density that in turn results in a potential drastic loss of burn power. Efforts are under way at the ASDEX Upgrade (AUG) tokamak aiming to provide real-time monitoring of pellet arrival and announcement of missed-out cases to the control systems. To further optimize the controllers, system identification experiments have been performed to identify the dynamic response of the system to the actuators.

Periodic edge localized modes (ELMs) are the non-linear consequences of pressure-gradient-driven ballooning modes and current-driven peeling modes becoming unstable in the pedestal region of high confinement fusion plasmas. In future tokamaks like ITER, large ELMs are foreseen to severely affect the lifetime of wall components as they transiently deposit large amounts of heat onto a narrow region at the divertor targets. Several strategies exist for avoidance, suppression, or mitigation of these instabilities, such as the naturally ELM-free quiescent H-mode (QH-mode). In the present article, an ASDEX Upgrade equilibrium that features a QH-mode is investigated through non-linear extended MHD simulations covering the dynamics over tens of milliseconds. The equilibrium is close to the ideal peeling limit and non-linearly develops saturated modes at the edge of the plasma. A dominant toroidal mode number of n=1 is found, for which the characteristic features of the edge harmonic oscillation are recovered. The saturated modes contribute to heat and particle transport preventing pedestal build-up to the ELM triggering threshold. The non-linear dynamics of the mode, in particular its interaction with the evolution of the edge safety factor is studied, which suggest a possible new saturation mechanism for the QH-mode. The simulations show good qualitative and quantitative agreement to experiments in AUG. In particular, the processes leading to the termination of QH-mode above a density threshold is studied, which results in the transition into an ELM regime. In the vicinity of this threshold, limit cycle oscillations are observed.